Arrhythmia　is　a　common　abnormality　of　cardiac　electrical　activity.　Arrhythmia　classification　has　enormous　significance　for　the　clinical　diagnosis　of　cardiovascular　diseases.　In　this　study,　a　method　of　multi-domain　electrocardiogram　feature　extraction　was　put　forward　as　to　classify　arrhythmia　precisely.　The　RR　intervals　were　extracted　as　time　domain　feature.　The　fifth　level　approximation　coefficients　of　wavelet　decomposition　were　adopted　to　represent　frequency　domain　feature.　Besides,　the　sample　entropy　values　of　six　wavelet　coefficients　were　employed　as　nonlinear　feature.　These　three　features　were　fed　to　classifier　for　automated　diagnosis.　Furthermore,　ten-fold　cross-validation　scheme　was　adopted　to　train　and　test　classifier　whose　parameters　were　optimized　by　genetic　algorithm.　In　this　study,　eight　classes　of　the　most　frequently　occurring　arrhythmia　from　MIT-BIH　arrhythmia　database　were　validated.　The　result　turned　out　that　the　SVM　classifier　yields　an　average　accuracy　of　99.70%.　Compared　with　the　existing　methods,　the　proposed　method　shows　better　results.　©　2019　IOP　Publishing　Ltd.　All　rights　reserved.